The following section provides an example of possible workflow. It is important to note that these are indeed examples of the software’s capabilities and are not intended to be used as scientific advice in a spatial conservation planning process. It is the user’s responsibility to ensure that all analysis decisions are valid.
library(sf)
library(leaflet)
library(tmap)
library(tidyverse)
library(DT)
# set default projection for leaflet
proj <- "+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs"
Download the example project folder. This folder contains the Marxan Connect Project file, the input data, and the output data from this example. Feel free to follow along using Marxan Connect by loading tutorial.MarCon.
Before adding connectivity to the mix, let’s have a look at the ‘traditional’ Marxan files. The files include hexagonal planning units that cover the Great Barrier Reef and we’ve identified a few bioregion types for which we’ve set conservation targets.
spec.datspec <- read.csv("tutorial/CSM_landscape/input/spec.dat")
datatable(spec,rownames = FALSE, options = list(searching = FALSE))
puvspr.datpuvspr <- read.csv("tutorial/CSM_landscape/input/puvspr.dat")
datatable(puvspr,rownames = FALSE, options = list(searching = FALSE))
pu.datpu <- read.csv("tutorial/CSM_landscape/input/pu.dat")
datatable(pu,rownames = FALSE, options = list(searching = FALSE))
puvspr_wide <- puvspr %>%
left_join(select(spec,"id","name"),
by=c("species"="id")) %>%
select(-species) %>%
spread(key="name",value="amount")
# planning units with output
output <- read.csv("tutorial/CSM_landscape/output/pu_no_connect.csv") %>%
mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),
best_solution = as.logical(best_solution)) %>%
st_as_sf() %>%
left_join(puvspr_wide,by=c("FID"="pu"))
map <- leaflet(output) %>%
addTiles()
groups <- names(select(output,-best_solution,-select_freq))[c(-1,-2,-length(names(output)))]
for(i in groups){
z <- unlist(data.frame(output)[i])
if(is.numeric(z)){
pal <- colorBin("YlOrRd", domain = z)
}else{
pal <- colorFactor("YlOrRd", domain = z)
}
map = map %>%
addPolygons(fillColor = ~pal(z),
fillOpacity = 0.6,
weight=0.5,
color="white",
group=i,
label = as.character(z)) %>%
addLegend(pal = pal,
values = z,
title = i,
group = i,
position="bottomleft")
}
map <- map %>%
addLayersControl(overlayGroups = groups,
options = layersControlOptions(collapsed = FALSE))
for(i in groups){
map <- map %>% hideGroup(i)
}
map %>%
showGroup("BIORE_102")
Let’s begin by examining the spatial layers we’ve added in order to incorporate connectivity into the Marxan analysis. Marxan Connect needs a shapefile for the planning units, the focus areas, and the avoidance areas.
# planning units
pu <- st_read("tutorial/CSM_landscape/hex_planning_units.shp") %>%
st_transform(proj)
#focus areas (IUCN level I or II protected areas)
fa <- st_read("tutorial/CSM_landscape/IUCN_IorII.shp") %>%
st_transform(proj)
# avoidance areas (ports)
aa <- st_read("tutorial/CSM_landscape/ports.shp") %>%
st_transform(proj)
p <- qtm(pu,fill = '#7570b3') +
qtm(fa,fill = '#1b9e77') +
qtm(aa,fill = '#d95f02')
tmap_leaflet(p) %>%
addLegend(position = "topright",
labels = c("Planning Units","Focus Areas (IUCN I or II)","Avoidance Areas (ports)"),
colors = c("#7570b3","#1b9e77","#d95f02"),
title = "Layers")
connectivity_matrix.csvThe connectivity data is at the ‘heart’ of Marxan Connect’s functionality. It allows the generation of new conservation features based on connectivity metrics.
For the sake of you web browser, this table only contains the 7 row and columns of the connectivity matrix. The real file has 653 X 653
conmat <- read.csv("tutorial/CSM_landscape/IsolationByDistance.csv")[1:7,]
datatable(conmat,rownames = FALSE, options = list(searching = FALSE))
In this example we’ve chosen to append the new connectivity based conservation metrics to the existing Marxan files.
spec_appended.datspec <- read.csv("tutorial/CSM_landscape/input/spec.dat")
datatable(spec,rownames = FALSE, options = list(searching = FALSE))
puvspr_appended.datpuvspr <- read.csv("tutorial/CSM_landscape/input/puvspr.dat")
datatable(puvspr,rownames = FALSE, options = list(searching = FALSE))
Finally, running Marxan with the connectivity conservation features and boundary definitions results in a different solution.
# planning units with output
output <- read.csv("tutorial/CSM_landscape/output/pu_connect.csv") %>%
mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),
best_solution = as.logical(best_solution),
# fa_included = as.logical(gsub("True",TRUE,.$fa_included)),
# aa_included = as.logical(gsub("True",TRUE,.$aa_included))
) %>%
st_as_sf()
map <- leaflet(output) %>%
addTiles()
groups <- names(output)[c(-1,-2,-length(names(output)))]
for(i in groups){
z <- unlist(data.frame(output)[i])
if(is.numeric(z)){
pal <- colorBin("YlOrRd", domain = z)
}else{
pal <- colorFactor("YlOrRd", domain = z)
}
map = map %>%
addPolygons(fillColor = ~pal(z),
fillOpacity = 0.6,
weight=0.5,
color="white",
group=i,
label = as.character(z)) %>%
addLegend(pal = pal,
values = z,
title = i,
group = i,
position="bottomleft")
}
map <- map %>%
addLayersControl(overlayGroups = groups,
options = layersControlOptions(collapsed = FALSE))
for(i in groups){
map <- map %>% hideGroup(i)
}
map %>%
showGroup("select_freq")
Here is the output of our example with no connectivity for comparison.
# planning units with output
output <- read.csv("tutorial/CSM_landscape/output/pu_no_connect.csv") %>%
mutate(geometry=st_as_sfc(geometry,"+proj=longlat +datum=WGS84"),
best_solution = as.logical(best_solution),
# fa_included = as.logical(gsub("True",TRUE,.$fa_included)),
# aa_included = as.logical(gsub("True",TRUE,.$aa_included))
) %>%
st_as_sf()
map <- leaflet(output) %>%
addTiles()
groups <- names(output)[c(-1,-2,-length(names(output)))]
for(i in groups){
z <- unlist(data.frame(output)[i])
if(is.numeric(z)){
pal <- colorBin("YlOrRd", domain = z)
}else{
pal <- colorFactor("YlOrRd", domain = z)
}
map = map %>%
addPolygons(fillColor = ~pal(z),
fillOpacity = 0.6,
weight=0.5,
color="white",
group=i,
label = as.character(z)) %>%
addLegend(pal = pal,
values = z,
title = i,
group = i,
position="bottomleft")
}
map <- map %>%
addLayersControl(overlayGroups = groups,
options = layersControlOptions(collapsed = FALSE))
for(i in groups){
map <- map %>% hideGroup(i)
}
map %>%
showGroup("select_freq")
# habitats <- st_read('../data/shapefiles/habitat.shp') %>%
# st_transform(proj)
# p <- tm_shape(habitats) +
# tm_fill("habitat",title="Habitat Type")
# tmap_leaflet(p)